[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Experian jobs](https://jobs.opennash.com/companies/experian/) · [Search jobs](/search/) Experian · United States # Expert Data Scientist, Fraud Risk Detection United States Remote Full-time Data & Analytics Listed on Experian’s official careers site. Posted October 12, 2026. Check that it is still open and apply there. [Apply on Experian’s careers site →](https://jobs.smartrecruiters.com/Experian/744000155129799-expert-data-scientist-fraud-risk-detection) [OpenNash Jobs](https://jobs.opennash.com/) / [Experian](https://jobs.opennash.com/companies/experian/) / Expert Data Scientist, Fraud Risk Detection ## Job description ### Overview Experian's Fraud Analytics & Commercialization operates across four main functions. These include client engagement analytics, scalable and custom analytics for financial institutions, fraud analytics consulting, and solution integrity and enablement for production-ready platforms. We're looking for a motivated Data Scientist to help build fraud detection models and features that identify high-risk activity while minimizing friction for legitimate customers. Core skills for this role include an eagerness to collaborate, and empathy. You will will dig into surprising signals in the data and to learn how that insight becomes a deployed model. You will help investigate the latest fraud patterns, build features, and train and evaluate machine learning models. You will work with senior data scientists and engineers starting with problem definition through feature engineering, experimentation, and deployment. You will be a developing programmer, ready to translate theoretical principles into production-ready solutions. We continue to sharpen through research and the engineering that turns those findings into tools and systems built for commercialization. This is a remote role and you will report into the Sr. Manager of Fraud Analytics. What you'll do Investigate large datasets, including exploratory analysis and fraud label development, to identify latest fraud patterns, attack methods, and behavioral signals. Translate ambiguous fraud and risk problems into clear hypotheses, analytical plans, model requirements, and measurable success criteria. Develop machine learning models for fraud detection across account opening, account takeover, and identity risk. Evaluate models using metrics like ROC/AUC/KS/Gini, precision/recall, fraud capture rate, false-positive rate, customer friction, and fraud losses prevented. Develop and validate predictive features using identity, transactional, consumer credit history, device, behavioral, temporal, velocity, network, and third-party data. Write clean, efficient, well-tested Python and PySpark code, and collaborate with teams to bring models and features into batch, retro, or real-time decisioning environments. Monitor feature quality, model performance, population changes, and fraud-pattern drift Design and present analyses for model behavior, tradeoffs, risks, and recommendations Follow appropriate standards for data privacy, model documentation, explainability, validation, and governance. ## Qualifications ### Qualifications At least - 3 years of experience in data science, machine learning, statistical modeling, or a related quantitative field - Bachelor's or advanced degree in computer science, statistics, engineering, data science, or another quantitative discipline - Direct experience developing fraud-detection, identity-risk, credit-risk, financial-crime, or other adversarial risk models. Demonstrated experience creating meaningful fraud features Proficiency in Python and PySpark, with experience writing modular and tested code for large datasets and distributed or cloud data systems. Experience using common data science and machine-learning tools such as pandas, NumPy, scikit-learn, XGBoost, TensorFlow, or comparable technologies. Knowledge of supervised learning, model evaluation, feature selection, statistical inference, experimentation, and model calibration. Experience navigating challenges common to fraud modeling, including class imbalance, delayed or incomplete labels, changing attack patterns, and model drift. Experience moving models beyond experimentation and into production, either directly or in close partnership with engineering teams. #LI-Remote ## At a glance - Posted by the employer: Oct 12, 2026 - First seen by OpenNash: Oct 12, 2026 - Last checked: Oct 19, 2026 - Listed for: 7 days - Location: United States - Work arrangement: Remote - Employment type: Full-time[Apply on Experian’s careers site →](https://jobs.smartrecruiters.com/Experian/744000155129799-expert-data-scientist-fraud-risk-detection) ## More jobs at Experian ### [Senior Data Modeler, Fraud Risk Detection](https://jobs.opennash.com/companies/experian/jobs/4ff3c645bd6dc6594ed4/) United States ### [Staff Architect - Data Platform (Remote)](https://jobs.opennash.com/companies/experian/jobs/48640a914051a876f58e/) United States ### [AI Native Staff Architect (Remote)](https://jobs.opennash.com/companies/experian/jobs/e2af6977b424c1ea8a9d/) United States ### [Client Executive - Fintech (West Coast or Northeast)](https://jobs.opennash.com/companies/experian/jobs/0c77424570809bc001b9/) United States ### [Client Executive, Community Lending Markets (Northwest Region)](https://jobs.opennash.com/companies/experian/jobs/b2219659639a0000636b/) United States ## Similar jobs at other companies ### [Principal Associate, Data Scientist - Customer Protection Debit Transaction Fraud Data Science](https://jobs.opennash.com/companies/capital-one-financial/jobs/b5a30ae1b4fda633eb13/) Capital One Financial · McLean, VA ### [Data Scientist-Fraud Strategy-Vice President](https://jobs.opennash.com/companies/jpmorganchase/jobs/15924164c39b1205185d/) JPMorganChase · Plano, TX, United States ### [Staff Scientist - Flow Cytometry Subject Matter Expert](https://jobs.opennash.com/companies/thermo-fisher-scientific/jobs/e193a105c2c45b50e404/) Thermo Fisher Scientific · Middleton, Wisconsin, United States of America ### [Applied Scientist, Payments & Fraud Prevention, AWS Payments & Fraud Prevention](https://jobs.opennash.com/companies/amazon/jobs/4d7cf0fff2c5f34498ed/) Amazon · US, NY, New York ### [Data Scientist](https://jobs.opennash.com/companies/huntington-bank/jobs/5f9db862d9e9bcf6d14e/) Huntington Bank · Columbus, OH OpenNash lists jobs from employers’ own careers sites and does not handle applications. Dates come from the employer where stated; “first seen” and “last checked” are our own observations. OpenNash Jobs · Official listings with dates and links to the employer. [Weekly hiring report](/reports/hiring/) · [Methodology](/methodology/)